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CORTEX seq-FISH: integration with scRNA-seq data Singh, Amrit


Dr. Amrit Singh ( is a post-doctoral research fellow at the PROOF Centre of Excellence. University of British Columbia, Pathology. He uses statistical methodologies to extract signals from big biological data. He have a background in biology, math, and programming (R/Python/Node) and has developed new methods to integrate multi-source biological data as part of the mixOmics data integration project . He is interested in using web and voice to develop interactive user interfaces for users to extract maximal information from their data. Dr. Amrit Singh work addressed the question if scRNA-seq data be overlaid onto seqFISH for resolution enhancement The published approach trained a multiclass SVM on the scRNAseq data and applied it to the seqFISH data to estimate the cell-types labels. My approach uses a penalized regression method (glmnet) with a semi-supervised approach in order to build a model using both the scRNAseq+seqFISH data. This strategy uses a recursive approach that involves multiple rounds of training glmnet models using labeled data (label and imputed) and predicting the cell-type labels of unlabeled data. At each iteration, cell-type labels with high confidence (probability > 0.5) are retained for the next iteration, where a new glmnet model is trained with the scRNAseq data and seqFISH data with imputed cell-type labels with high confidence. This process is repeated until all cell-types in the seqFISH data have been labeled or until 50 iterations have been reached (in order to reduce compute times). The advantage of this approach is that more data in used for model training such that the resulting model may generalize better to new data. The performance of this approach was estimated using cross-validation, using only the scRNAseq data as the test set. This work was done in collaboration with Prof Kim-Anh Le Cao (University of Melbourne) . Code is available at

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